This paper presents a new data-driven approach for the establishment of empirical models describing turbulent boundary layer wall-pressure spectra. Unlike other models presented in literature, the new models are not derived by extending previously existing ones, but are directly built from a given dataset through symbolic regression using a machine learning algorithm known as Gene Expression Programming. Two modifications of the GEP algorithm presented in literature are proposed in this work to cope with some issues that are specific to the modelling of wall pressure spectra: a new power terminal and a local optimization loop. The validity of the new approach is first demonstrated using as input a dataset synthesized following the Chase-Howe and Goody models. The method is then applied to experimental data for a flat plate boundary layer. The results indicate that the wall pressure model obtained with the proposed approach remains consistent with previous formulations for zero pressure gradient, while showing a better match with the data and suggesting new ways to predict the influence of moderate pressure gradient

本文提出了一种新的数据驱动方法，用于建立描述湍流边界层壁-压力谱的经验模型。与文献中介绍的其他模型不同，新模型不是通过扩展先前存在的模型而得出的，而是使用称为基因表达编程的机器学习算法通过符号回归直接从给定的数据集中构建的。在这项工作中，提出了对文献中提出的GEP算法的两种修改，以解决壁压力谱建模所特有的一些问题：新的电源终端和局部优化回路。首先使用按照Chase-Howe和Goody模型综合的数据集作为输入，证明了新方法的有效性。然后将该方法应用于平板边界层的实验数据。